The project aim is to study the properties of a new method for multiple comparisons in the multivariate general linear model (MGLM) setting. These MGLM models are widely used in the health sciences, primarily in the form of repeated measures/profile analyses. The dominant existing method for multiple comparisons in an MGLM is a multivariate generalization of the Scheffe method used in univariate analysis of variance. This method is extremely conservative. Its use is nearly always a gross waste of expensive and vital experimental information. The new procedure is expected to perform well in terms of the usual (1-alpha)x100% confidence requirement. There is concrete evidence (based on efficiency studies of univariate analogs of the two methods) that it may provide sample size savings of 30% or more over the Scheffe-type method.